The Abstraction and Reasoning Corpus (ARC) is a general artificial intelligence benchmark that poses difficulties for pure machine learning methods due to its requirement for fluid intelligence with a focus on reasoning and abstraction. In this work, we introduce an ARC solver, Generalized Planning for Abstract Reasoning (GPAR). It casts an ARC problem as a generalized planning (GP) problem, where a solution is formalized as a planning program with pointers. We express each ARC problem using the standard Planning Domain Definition Language (PDDL) coupled with external functions representing object-centric abstractions. We show how to scale up GP solvers via domain knowledge specific to ARC in the form of restrictions over the actions model, predicates, arguments and valid structure of planning programs. Our experiments demonstrate that GPAR outperforms the state-of-the-art solvers on the object-centric tasks of the ARC, showing the effectiveness of GP and the expressiveness of PDDL to model ARC problems. The challenges provided by the ARC benchmark motivate research to advance existing GP solvers and understand new relations with other planning computational models. Code is available at github.com/you68681/GPAR.

通过将ARC问题转化为广义规划问题，我们引入了ARC求解器GPAR，采用指针的规划程序来解决ARC问题，利用表示对象中心抽象的外部函数结合标准规划域定义语言（PDDL）来表达每个ARC问题，并通过领域知识来扩展GP求解器，实验结果表明GPAR在ARC的对象中心任务上表现优于最先进的求解器，展示了GP的有效性和PDDL对建模ARC问题的表达能力。

通用规划用于抽象推理语料库